# Online Graph-Adaptive Learning with Scalability and Privacy

## TL;DR

This paper introduces a scalable, privacy-preserving online learning method for estimating nodal attributes in large, dynamic graphs using a multikernel approach that enables real-time updates without batch re-computation.

## Contribution

It proposes a novel multikernel-based online learning algorithm that efficiently updates nodal attribute estimates in large, evolving graphs while maintaining privacy through encrypted connectivity data.

## Key findings

- Effective on synthetic datasets
- Demonstrates scalability to large networks
- Maintains privacy with encrypted connectivity

## Abstract

Graphs are widely adopted for modeling complex systems, including financial, biological, and social networks. Nodes in networks usually entail attributes, such as the age or gender of users in a social network. However, real-world networks can have very large size, and nodal attributes can be unavailable to a number of nodes, e.g., due to privacy concerns. Moreover, new nodes can emerge over time, which can necessitate real-time evaluation of their nodal attributes. In this context, the present paper deals with scalable learning of nodal attributes by estimating a nodal function based on noisy observations at a subset of nodes. A multikernel-based approach is developed which is scalable to large-size networks. Unlike most existing methods that re-solve the function estimation problem over all existing nodes whenever a new node joins the network, the novel method is capable of providing real-time evaluation of the function values on newly-joining nodes without resorting to a batch solver. Interestingly, the novel scheme only relies on an encrypted version of each node's connectivity in order to learn the nodal attributes, which promotes privacy. Experiments on both synthetic and real datasets corroborate the effectiveness of the proposed methods.